This algorithm removes the dependency on the catalogue
size by avoiding the computation of \(Z_\theta (x_i)\) .
The cost for this is that the estimator is now biased with a bias decreasing with how close the proposal \(q\)  is to the policy \(\pi _\theta \)  [1]}. This means that if we want to exploit self normalized importance sampling efficiently, we need to have access to a proposal \(q\)  that is fast to sample from, easy to evaluate as its needed to compute the weights \({\omega }_s\)  and close to the actual policy \(\pi _\theta \)  to reduce the bias and the variance of the method.
